Principled Asymmetric Boosting Approaches to Rapid Training and Classification in Face Detection Minh-Tri Pham Ph.D. Candidate and Research Associate Nanyang Technological University, Singapore presented by Outline Motivation Contributions AAutomatic Selection of Asymmetric Goal FFast Weak Classifier Learning OOnline Asymmetric Boosting GGeneralization Bounds on the Asymmetric Error Future Work Summary Motivation Contributions Automatic Selection of Asymmetric Goal Fast Weak Classifier Learning Online Asymmetric Boosting Generalization Bounds on the Asymmetric Error Future Work Summary Problem Application Face recognition Application 3D face reconstruction Application Camera auto-focusing Application Windows face logon Lenovo Veriface Technology Windows face logon Lenovo Veriface Technology Appearance-based Approach Scan image with probe window patch (x,y,s) at different positions and scales Binary classify each patch into face, or non-face Desired output state: (x,y,s) containing face 0 1 Most popular approach Viola-Jones 01-04, Li et.al. 02, Wu et.al. 04, Brubaker et.al. 04, Liu et.al. 04, Xiao et.al 04, Bourdev-Brandt 05, Mita et.al. 05, Huang et.al. 05 07, Wu et.al. 05, Grabner et.al. 05-07, And many more Most popular approach Viola-Jones 01-04, Li et.al. 02, Wu et.al. 04, Brubaker et.al. 04, Liu et.al. 04, Xiao et.al 04, Bourdev-Brandt 05, Mita et.al. 05, Huang et.al. 05 07, Wu et.al. 05, Grabner et.al. 05-07, And many more Appearance-based Approach Statistics: 6,950,440 patches in a 320x240 image P(face) < Key requirement: A very fast classifier 0 1 A very fast classifier Cascade of non-face rejectors: F1F1 F1F1 F2F2 F2F2 FNFN FNFN . pass reject face non-face F1F1 F1F1 F2F2 F2F2 FNFN FNFN . pass reject face non-face Cascade of non-face rejectors: F 1, F 2, , F N : asymmetric classifiers FRR(F k ) 0 FAR(F k ) as small as possible (e.g. 0.5 0.8) A very fast classifier F1F1 F1F1 F2F2 F2F2 non-face F1F1 F1F1 F2F2 F2F2 FNFN FNFN face F1F1 F1F1 F2F2 F2F2 non-face F1F1 F1F1 F2F2 F2F2 FNFN FNFN face F1F1 F1F1 F2F2 F2F2 non-face F1F1 F1F1 F2F2 F2F2 FNFN FNFN face F1F1 F1F1 F2F2 F2F2 non-face F1F1 F1F1 F2F2 F2F2 FNFN FNFN face Cascade of non-face rejectors: F 1, F 2, , F N : asymmetric classifiers FRR(F k ) 0 FAR(F k ) as small as possible (e.g. 0.5 0.8) A very fast classifier F1F1 F1F1 FNFN FNFN . pass reject face non-face F2F2 F2F2 A strong combination of weak classifiers: Non-face Rejector f 1,1, f 1,2, , f 1,K : weak classifiers : threshold pass reject F1F1 F1F1 yes no f 1,1 f 1,2 f 1,K > ? Boosting Weak Classifier Learner 1 Weak Classifier Learner 2 Wrongly classified Correctly classified : negative example : positive example Stage 1Stage 2 Asymmetric Boosting Weak Classifier Learner 1 Weak Classifier Learner 2 : negative example : positive example Stage 1Stage 2 Weight positives times more than negatives pass reject F1F1 F1F1 yes no f 1,2 f 1,K > ? A strong combination of weak classifiers: Non-face Rejector f 1,1, f 1,2, , f 1,K : weak classifiers : threshold f 1,1 pass reject F1F1 F1F1 yes no f 1,2 f 1,K > ? A strong combination of weak classifiers: Non-face Rejector f 1,1, f 1,2, , f 1,K : weak classifiers : threshold f 1,1 Classify a Haar-like feature value Weak classifier input patch feature value v score Classify a Haar-like feature value Weak classifier input patch feature value v score Requires too much intervention from experts Main issues Cascade of non-face rejectors: F 1, F 2, , F N : asymmetric classifiers FRR(F k ) 0 FAR(F k ) as small as possible (e.g. 0.5 0.8) A very fast classifier F1F1 F1F1 FNFN FNFN . pass reject face non-face F2F2 F2F2 How to choose bounds for FRR(F k ) and FAR(F k )? Asymmetric Boosting Weak Classifier Learner 1 Weak Classifier Learner 2 : negative example : positive example Stage 1Stage 2 Weight positives times more than negatives How to choose ? pass reject F1F1 F1F1 yes no f 1,2 f 1,K > ? A strong combination of weak classifiers: Non-face Rejector f 1,1, f 1,2, , f 1,K : weak classifiers : threshold f 1,1 How to choose ? Requires too much intervention from experts Very long learning time Main issues Classify a Haar-like feature value Weak classifier input patch feature value v score 10 minutes to learn a weak classifier Requires too much intervention from experts Very long learning time To learn a face detector ( 4000 weak classifiers): 4,000 * 10 minutes 1 month Only suitable for objects with small shape variance Main issues Outline Motivation Contributions Automatic Selection of Asymmetric Goal Fast Weak Classifier Learning Online Asymmetric Boosting Generalization Bounds on the Asymmetric Error Future Work Summary Outline Motivation Contributions Automatic Selection of Asymmetric Goal Fast Weak Classifier Learning Online Asymmetric Boosting Generalization Bounds on the Asymmetric Error Future Work Summary Outline Motivation Contributions Automatic Selection of Asymmetric Goal Fast Weak Classifier Learning Online Asymmetric Boosting Generalization Bounds on the Asymmetric Error Future Work Summary Detection with Multi-exit Asymmetric Boosting CVPR08 poster paper: Minh-Tri Pham and Viet-Dung D. Hoang and Tat-Jen Cham. Detection with Multi-exit Asymmetric Boosting. In Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, Alaska, Won Travel Grant Award Problem overview Common appearance-based approach: F 1, F 2, , F N : boosted classifiers f 1,1, f 1,2, , f 1,K : weak classifiers : threshold F1F1 F1F1 F2F2 F2F2 FNFN FNFN . pass reject object non-object pass reject F1F1 F1F1 yes no f 1,1 f 1,2 f 1,K > ? Objective Find f 1,1, f 1,2, , f 1,K, and such that: K is minimized proportional to F 1 s evaluation time pass reject F1F1 F1F1 yes no f 1,1 f 1,2 f 1,K > ? Existing trends (1) Idea For k from 1 until convergence: Let Learn new weak classifier f 1,k (x): Let Adjust to see if we can achieve FAR(F 1 )